A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts
Numerous researchers have worked on the knowledge-based semantics of words to clarify the ambiguity of (https://github.com/alimuttaleb/Ali-Muttaleb/blob/master/Synonym.txt) synonyms in various natural-language processing fields, such as Wikipedia, websites, and social networks. This paper attempts t...
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| Format: | Conference or Workshop Item |
| Language: | English |
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Springer
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/28450/1/A%20Semantic%20Taxonomy%20for%20Weighting%20Assumptions%20to%20Reduce%20Feature%20Selection%20from%20Social%20Media%20and%20Forum%20Posts.pdf |
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| author | Ali Muttaleb, Hasan Rassem, Taha H. Noorhuzaimi@Karimah, Mohd Noor Ahmed Muttaleb, Hasan |
| author_facet | Ali Muttaleb, Hasan Rassem, Taha H. Noorhuzaimi@Karimah, Mohd Noor Ahmed Muttaleb, Hasan |
| author_sort | Ali Muttaleb, Hasan |
| collection | UMP |
| description | Numerous researchers have worked on the knowledge-based semantics of words to clarify the ambiguity of (https://github.com/alimuttaleb/Ali-Muttaleb/blob/master/Synonym.txt) synonyms in various natural-language processing fields, such as Wikipedia, websites, and social networks. This paper attempts to clarify ambiguities in the lexical semantics of taxonomy in social media. It proposes a new knowledge-based semantic representation approach that can handle ambiguity and high dimensionality issues in text mining. The proposed approach consists of two main components, namely, a feature-based method for incorporating the relationships between lexical sources and a topic-based reduction method to overcome high dimensionality issues. These components help weight and reduce the relevant features of a concept. The proposed approach captures further lexical semantic similarity between words. It also evaluates the use of (https://wordnet.princeton.edu) WordNet 3.1 in text clustering and constant weighting assumption in the feature-based method used to select concepts/words from social media. To address ambiguity, the semantics of concepts with small feature subset size reduction are represented, and the performance of the semantic similarity measurement is improved. The proposed method evaluates word semantic similarity using the (https://github.com/alimuttaleb/semantictaxonomy/blob/master/mc30.txt) MC30 dataset in WordNet and obtains the following results for semantic representation: r = 0.82, p = 0.81, m = 0.81, and nz = 0.96. |
| first_indexed | 2024-03-06T12:42:46Z |
| format | Conference or Workshop Item |
| id | UMPir28450 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:42:46Z |
| publishDate | 2020 |
| publisher | Springer |
| record_format | dspace |
| spelling | UMPir284502020-07-13T06:16:37Z http://umpir.ump.edu.my/id/eprint/28450/ A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts Ali Muttaleb, Hasan Rassem, Taha H. Noorhuzaimi@Karimah, Mohd Noor Ahmed Muttaleb, Hasan QA75 Electronic computers. Computer science QA76 Computer software Numerous researchers have worked on the knowledge-based semantics of words to clarify the ambiguity of (https://github.com/alimuttaleb/Ali-Muttaleb/blob/master/Synonym.txt) synonyms in various natural-language processing fields, such as Wikipedia, websites, and social networks. This paper attempts to clarify ambiguities in the lexical semantics of taxonomy in social media. It proposes a new knowledge-based semantic representation approach that can handle ambiguity and high dimensionality issues in text mining. The proposed approach consists of two main components, namely, a feature-based method for incorporating the relationships between lexical sources and a topic-based reduction method to overcome high dimensionality issues. These components help weight and reduce the relevant features of a concept. The proposed approach captures further lexical semantic similarity between words. It also evaluates the use of (https://wordnet.princeton.edu) WordNet 3.1 in text clustering and constant weighting assumption in the feature-based method used to select concepts/words from social media. To address ambiguity, the semantics of concepts with small feature subset size reduction are represented, and the performance of the semantic similarity measurement is improved. The proposed method evaluates word semantic similarity using the (https://github.com/alimuttaleb/semantictaxonomy/blob/master/mc30.txt) MC30 dataset in WordNet and obtains the following results for semantic representation: r = 0.82, p = 0.81, m = 0.81, and nz = 0.96. Springer 2020 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/28450/1/A%20Semantic%20Taxonomy%20for%20Weighting%20Assumptions%20to%20Reduce%20Feature%20Selection%20from%20Social%20Media%20and%20Forum%20Posts.pdf Ali Muttaleb, Hasan and Rassem, Taha H. and Noorhuzaimi@Karimah, Mohd Noor and Ahmed Muttaleb, Hasan (2020) A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts. In: 4th International Conference of Reliable Information and Communication Technology, IRICT 2019 , 22-23 September 2019 , Johor Bahru, Malaysia. pp. 407-419., 1073. ISSN 2194-5357 ISBN 978-303033581-6 (Published) https://doi.org/10.1007/978-3-030-33582-3_39 https://doi.org/10.1007/978-3-030-33582-3_39 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software Ali Muttaleb, Hasan Rassem, Taha H. Noorhuzaimi@Karimah, Mohd Noor Ahmed Muttaleb, Hasan A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts |
| title | A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts |
| title_full | A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts |
| title_fullStr | A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts |
| title_full_unstemmed | A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts |
| title_short | A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts |
| title_sort | semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts |
| topic | QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/28450/1/A%20Semantic%20Taxonomy%20for%20Weighting%20Assumptions%20to%20Reduce%20Feature%20Selection%20from%20Social%20Media%20and%20Forum%20Posts.pdf |
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